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We study the self-directed (SD) learning model.In this model a learner chooses examples, guesses theirclassification and receives immediate feedback indicatingthe correctness of its guesses.We consider several fundamental questions concerning this model:the parameters of a task that determine the cost oflearning, the computational complexity of a student, andthe relationship between this model and the teacher-directed (TD) learning model.We answer the open problem of relating the cost ofself-directed learning to the VC-dimension by showing that no suchrelation exists. Furthermore, we refute the conjecture that forthe intersection-closed case, the cost of self-directed learning isbounded by the VC-dimension.We also show that the cost ofSD learning may be arbitrarily higher that that of TD learning.Finally, we discuss the number ofqueries needed for learning in this model and itsrelationship to the number of mistakesthe student incurs.We prove a trade-off formula showing that an algorithm that makesfewer queries throughout its learning process, necessarilysuffers a higher number of mistakes.

Self-Directed Learning and Its Relation to the VC-Dimension and to Teacher-Directed Learning